Instructions to use SmuFypTeam5/GradingQlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmuFypTeam5/GradingQlora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "SmuFypTeam5/GradingQlora")

Transformers

How to use SmuFypTeam5/GradingQlora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SmuFypTeam5/GradingQlora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SmuFypTeam5/GradingQlora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SmuFypTeam5/GradingQlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SmuFypTeam5/GradingQlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmuFypTeam5/GradingQlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SmuFypTeam5/GradingQlora

SGLang

How to use SmuFypTeam5/GradingQlora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SmuFypTeam5/GradingQlora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmuFypTeam5/GradingQlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SmuFypTeam5/GradingQlora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmuFypTeam5/GradingQlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SmuFypTeam5/GradingQlora with Docker Model Runner:
```
docker model run hf.co/SmuFypTeam5/GradingQlora
```

GradingQlora / selection.json

awork2103

Upload evaluated Qwen3.5-9B adapter: seed 2026, best_loss

7316055 verified about 1 month ago

Raw History Blame Contribute Delete

1.44 kB

{
  "selection": "best_loss",
  "metric": "eval_loss",
  "maximize": false,
  "value": 0.08017373085021973,
  "step": 392,
  "epoch": 1.0051397365884998,
  "training_seed": 2026,
  "split_seed": 2026,
  "adapter_dir": "qwen35-9b-grading-checkpoint-comparison/run-20261009T143514-201079Z/seed_2026/best_loss",
  "validation_metrics": {
    "eval_loss": 0.08017373085021973,
    "eval_grading_examples": 269,
    "eval_grading_valid_examples": 269,
    "eval_grading_recovered_output_rate": 0.0,
    "eval_grading_invalid_output_rate": 0.0,
    "eval_grading_exact_match_accuracy": 0.7174721189591078,
    "eval_grading_per_criterion_accuracy": 0.8262548262548263,
    "eval_grading_precision": 0.8563049853372434,
    "eval_grading_recall": 0.8768768768768769,
    "eval_grading_f1": 0.8664688427299704,
    "eval_grading_final_score_exact_accuracy": 0.758364312267658,
    "eval_grading_final_score_within_one_accuracy": 0.9888475836431226,
    "eval_grading_final_score_mae": 0.2527881040892193,
    "eval_grading_final_score_rmse": 0.5244930283645093,
    "eval_grading_final_score_pearson": 0.7658867012264351,
    "eval_grading_final_score_qwk": 0.7626225285601851,
    "eval_grading_question_count": 9,
    "eval_grading_question_macro_exact_match_accuracy": 0.7057324291998621,
    "eval_grading_question_macro_per_criterion_accuracy": 0.8368088075303911,
    "eval_grading_question_macro_criterion_f1": 0.8543485592790837
  }
}

	{
	"selection": "best_loss",
	"metric": "eval_loss",
	"maximize": false,
	"value": 0.08017373085021973,
	"step": 392,
	"epoch": 1.0051397365884998,
	"training_seed": 2026,
	"split_seed": 2026,
	"adapter_dir": "qwen35-9b-grading-checkpoint-comparison/run-20261009T143514-201079Z/seed_2026/best_loss",
	"validation_metrics": {
	"eval_loss": 0.08017373085021973,
	"eval_grading_examples": 269,
	"eval_grading_valid_examples": 269,
	"eval_grading_recovered_output_rate": 0.0,
	"eval_grading_invalid_output_rate": 0.0,
	"eval_grading_exact_match_accuracy": 0.7174721189591078,
	"eval_grading_per_criterion_accuracy": 0.8262548262548263,
	"eval_grading_precision": 0.8563049853372434,
	"eval_grading_recall": 0.8768768768768769,
	"eval_grading_f1": 0.8664688427299704,
	"eval_grading_final_score_exact_accuracy": 0.758364312267658,
	"eval_grading_final_score_within_one_accuracy": 0.9888475836431226,
	"eval_grading_final_score_mae": 0.2527881040892193,
	"eval_grading_final_score_rmse": 0.5244930283645093,
	"eval_grading_final_score_pearson": 0.7658867012264351,
	"eval_grading_final_score_qwk": 0.7626225285601851,
	"eval_grading_question_count": 9,
	"eval_grading_question_macro_exact_match_accuracy": 0.7057324291998621,
	"eval_grading_question_macro_per_criterion_accuracy": 0.8368088075303911,
	"eval_grading_question_macro_criterion_f1": 0.8543485592790837
	}
	}